The imaging and dosimetric capabilities of a CT/MR-suitable, anatomically adaptive, shielded intracavitary brachytherapy applicator for the treatment of cervical cancer

Introduction. Investigations into the shortcomings of current intracavitary brachytherapy (ICBT) technology has lead us to design an Anatomically Adaptive Applicator (A3). The goal of this work was to design and characterize the imaging and dosimetric capabilities of this device. The A3 design incorporates a single shield that can both rotate and translate within the colpostat. We hypothesized that this feature, coupled with specific A3 component construction materials and imaging techniques, would facilitate artifact-free CT and MR image acquisition. In addition, by shaping the delivered dose distribution via the A3 movable shield, dose delivered to the rectum will be less compared to equivalent treatments utilizing current state-of-the-art ICBT applicators. ^ Method and materials. A method was developed to facilitate an artifact-free CT imaging protocol that used a "step-and-shoot" technique: pausing the scanner midway through the scan and moving the A 3 shield out of the path of the beam. The A3 CT imaging capabilities were demonstrated acquiring images of a phantom that positioned the A3 and FW applicators in a clinically-applicable geometry. Artifact-free MRI imaging was achieved by utilizing MRI-compatible ovoid components and pulse-sequences that minimize susceptibility artifacts. Artifacts were qualitatively compared, in a clinical setup. For the dosimetric study, Monte-Carlo (MC) models of the A3 and FW (shielded and unshielded) applicators were validated. These models were incorporated into a MC model of one cervical cancer patient ICBT insertion, using 192Ir (mHDR v2 source). The A3 shield's rotation and translation was adjusted for each dwell position to minimize dose to the rectum. Superposition of dose to rectum for all A3 dwell sources (4 per ovoid) was applied to obtain a comparison of equivalent FW treatments. Rectal dose-volume histograms (absolute and HDR/PDR biologically effective dose (BED)) and BED to 2 cc (BED2cc ) were determined for all applicators and compared. ^ Results. Using a "step-and-shoot" CT scanning method and MR compliant materials and optimized pulse-sequences, images of the A 3 were nearly artifact-free for both modalities. The A3 reduced BED2cc by 18.5% and 7.2% for a PDR treatment and 22.4% and 8.7% for a HDR treatment compared to treatments delivered using an uFW and sFW applicator, respectively. ^ Conclusions. The novel design of the A3 facilitated nearly artifact-free image quality for both CT and MR clinical imaging protocols. The design also facilitated a reduction in BED to the rectum compared to equivalent ICBT treatments delivered using current, state-of-the-art applicators. ^

Bayesian adaptive designs for drug combinations in early phase clinical trials

Treating patients with combined agents is a growing trend in cancer clinical trials. Evaluating the synergism of multiple drugs is often the primary motivation for such drug-combination studies. Focusing on the drug combination study in the early phase clinical trials, our research is composed of three parts: (1) We conduct a comprehensive comparison of four dose-finding designs in the two-dimensional toxicity probability space and propose using the Bayesian model averaging method to overcome the arbitrariness of the model specification and enhance the robustness of the design; (2) Motivated by a recent drug-combination trial at MD Anderson Cancer Center with a continuous-dose standard of care agent and a discrete-dose investigational agent, we propose a two-stage Bayesian adaptive dose-finding design based on an extended continual reassessment method; (3) By combining phase I and phase II clinical trials, we propose an extension of a single agent dose-finding design. We model the time-to-event toxicity and efficacy to direct dose finding in two-dimensional drug-combination studies. We conduct extensive simulation studies to examine the operating characteristics of the aforementioned designs and demonstrate the designs' good performances in various practical scenarios.^

Bayesian Adaptive Designs for Early Phase Clinical Trials

My dissertation focuses mainly on Bayesian adaptive designs for phase I and phase II clinical trials. It includes three specific topics: (1) proposing a novel two-dimensional dose-finding algorithm for biological agents, (2) developing Bayesian adaptive screening designs to provide more efficient and ethical clinical trials, and (3) incorporating missing late-onset responses to make an early stopping decision. Treating patients with novel biological agents is becoming a leading trend in oncology. Unlike cytotoxic agents, for which toxicity and efficacy monotonically increase with dose, biological agents may exhibit non-monotonic patterns in their dose-response relationships. Using a trial with two biological agents as an example, we propose a phase I/II trial design to identify the biologically optimal dose combination (BODC), which is defined as the dose combination of the two agents with the highest efficacy and tolerable toxicity. A change-point model is used to reflect the fact that the dose-toxicity surface of the combinational agents may plateau at higher dose levels, and a flexible logistic model is proposed to accommodate the possible non-monotonic pattern for the dose-efficacy relationship. During the trial, we continuously update the posterior estimates of toxicity and efficacy and assign patients to the most appropriate dose combination. We propose a novel dose-finding algorithm to encourage sufficient exploration of untried dose combinations in the two-dimensional space. Extensive simulation studies show that the proposed design has desirable operating characteristics in identifying the BODC under various patterns of dose-toxicity and dose-efficacy relationships. Trials of combination therapies for the treatment of cancer are playing an increasingly important role in the battle against this disease. To more efficiently handle the large number of combination therapies that must be tested, we propose a novel Bayesian phase II adaptive screening design to simultaneously select among possible treatment combinations involving multiple agents. Our design is based on formulating the selection procedure as a Bayesian hypothesis testing problem in which the superiority of each treatment combination is equated to a single hypothesis. During the trial conduct, we use the current values of the posterior probabilities of all hypotheses to adaptively allocate patients to treatment combinations. Simulation studies show that the proposed design substantially outperforms the conventional multi-arm balanced factorial trial design. The proposed design yields a significantly higher probability for selecting the best treatment while at the same time allocating substantially more patients to efficacious treatments. The proposed design is most appropriate for the trials combining multiple agents and screening out the efficacious combination to be further investigated. The proposed Bayesian adaptive phase II screening design substantially outperformed the conventional complete factorial design. Our design allocates more patients to better treatments while at the same time providing higher power to identify the best treatment at the end of the trial. Phase II trial studies usually are single-arm trials which are conducted to test the efficacy of experimental agents and decide whether agents are promising to be sent to phase III trials. Interim monitoring is employed to stop the trial early for futility to avoid assigning unacceptable number of patients to inferior treatments. We propose a Bayesian single-arm phase II design with continuous monitoring for estimating the response rate of the experimental drug. To address the issue of late-onset responses, we use a piece-wise exponential model to estimate the hazard function of time to response data and handle the missing responses using the multiple imputation approach. We evaluate the operating characteristics of the proposed method through extensive simulation studies. We show that the proposed method reduces the total length of the trial duration and yields desirable operating characteristics for different physician-specified lower bounds of response rate with different true response rates.

CHARACTERIZATION AND TREATMENT OF A NOVEL MOUSE MODEL OF TSC-ASSOCIATED AUTISM

Tuberous sclerosis complex (TSC) is a dominant tumor suppressor disorder caused by mutations in either TSC1 or TSC2. The proteins of these genes form a complex to inhibit the mammalian target of rapamycin complex 1 (mTORC1), which controls protein translation and cell growth. TSC causes substantial neuropathology, often leading to autism spectrum disorders (ASDs) in up to 60% of patients. The anatomic and neurophysiologic links between these two disorders are not well understood. However, both disorders share cerebellar abnormalities. Therefore, we have characterized a novel mouse model in which the Tsc2 gene was selectively deleted from cerebellar Purkinje cells (Tsc2f/-;Cre). These mice exhibit progressive Purkinje cell degeneration. Since loss of Purkinje cells is a well-reported postmortem finding in patients with ASD, we conducted a series of behavior tests to assess if Tsc2f/-;Cre mice displayed autistic-like deficits. Using the three chambered social choice assay, we found that Tsc2f/-;Cre mice showed behavioral deficits, exhibiting no preference between a stranger mouse and an inanimate object, or between a novel and a familiar mouse. Tsc2f/-;Cre mice also demonstrated increased repetitive behavior as assessed with marble burying activity. Altogether, these results demonstrate that loss of Tsc2 in Purkinje cells in a haploinsufficient background lead to behavioral deficits that are characteristic of human autism. Therefore, Purkinje cells loss and/or dysfunction may be an important link between TSC and ASD. Additionally, we have examined some of the cellular mechanisms resulting from mutations in Tsc2 leading to Purkinje cell death. Loss of Tsc2 led to upregulation of mTORC1 and increased cell size. As a consequence of increased protein synthesis, several cellular stress pathways were upregulated. Principally, these included altered calcium signaling, oxidative stress, and ER stress. Likely as a consequence of ER stress, there was also upregulation of ubiquitin and autophagy. Excitingly, treatment with an mTORC1 inhibitor, rapamycin attenuated mTORC1 activity and prevented Purkinje cell death by reducing of calcium signaling, the ER stress response, and ubiquitin. Remarkably, rapamycin treatment also reversed the social behavior deficits, thus providing a promising potential therapy for TSC-associated ASD.

Bayesian adaptive designs in early phase clinical trials

There are two practical challenges in the phase I clinical trial conduct: lack of transparency to physicians, and the late onset toxicity. In my dissertation, Bayesian approaches are used to address these two problems in clinical trial designs. The proposed simple optimal designs cast the dose finding problem as a decision making process for dose escalation and deescalation. The proposed designs minimize the incorrect decision error rate to find the maximum tolerated dose (MTD). For the late onset toxicity problem, a Bayesian adaptive dose-finding design for drug combination is proposed. The dose-toxicity relationship is modeled using the Finney model. The unobserved delayed toxicity outcomes are treated as missing data and Bayesian data augment is employed to handle the resulting missing data. Extensive simulation studies have been conducted to examine the operating characteristics of the proposed designs and demonstrated the designs' good performances in various practical scenarios.^

Adaptive clinical trial design for targeted therapy development

The development of targeted therapy involve many challenges. Our study will address some of the key issues involved in biomarker identification and clinical trial design. In our study, we propose two biomarker selection methods, and then apply them in two different clinical trial designs for targeted therapy development. In particular, we propose a Bayesian two-step lasso procedure for biomarker selection in the proportional hazards model in Chapter 2. In the first step of this strategy, we use the Bayesian group lasso to identify the important marker groups, wherein each group contains the main effect of a single marker and its interactions with treatments. In the second step, we zoom in to select each individual marker and the interactions between markers and treatments in order to identify prognostic or predictive markers using the Bayesian adaptive lasso. In Chapter 3, we propose a Bayesian two-stage adaptive design for targeted therapy development while implementing the variable selection method given in Chapter 2. In Chapter 4, we proposed an alternate frequentist adaptive randomization strategy for situations where a large number of biomarkers need to be incorporated in the study design. We also propose a new adaptive randomization rule, which takes into account the variations associated with the point estimates of survival times. In all of our designs, we seek to identify the key markers that are either prognostic or predictive with respect to treatment. We are going to use extensive simulation to evaluate the operating characteristics of our methods.^